A financial institution is building machine learning models to assess credit risk by analyzing structured data, such as transaction history and customer profiles, as well as unstructured data, such as customer reviews and support interactions. The data science team needs to apply appropriate feature engineering techniques for each data type to ensure accurate risk predictions. What is the key difference in feature engineering tasks for structured data compared to unstructured data in the context of machine learning? (Select TWO.)